The technical challenges for counting lumber boards, using automated computing and an image from a camera are numerous. For example, there may be variations in illumination, viewpoint, scale (distance between camera and bundle), board size, board shape, and board texture as well as blurring, shadows, distractors (e.g., marks written on lumber), and other irregularities in appearance and structure.
To account for the large number of variations, existing systems for automated board counting rely on mounting systems with fixed cameras and illumination. Such systems provide a highly-controlled environment for image capture. While these systems are effective, they are expensive to configure and operate. Moreover, additional resources are necessary to position the boards precisely so that the boards can be properly imaged by the camera within the mounting system, which may be time consuming.
Handheld devices with cameras are now commonly available. For example, many people now carry smartphones that include a camera. However, images captured using such devices may be more susceptible to the types of variations described above that make automated analysis difficult. Hence, mobile devices with cameras are ineffective in addressing such challenges.